Teaching X‐ray interpretation: selecting the radiographs by the target population
Bibliographic record
Abstract
CONTEXT: The unbiased selection of images representing a spectrum of diagnostic difficulty is an important first step in designing effective assessment and teaching interventions for X-ray interpretation. OBJECTIVES: This study aimed to develop a scale that would reliably differentiate more difficult X-rays from those that are easier to interpret. METHODS: After pilot testing, an X-ray difficulty scale (XRDS) was developed. Raters of different learner levels from two universities were presented with 20 chest X-rays (CXRs) and asked to read them and then to answer the scale questions that would help to differentiate the level of difficulty of interpretation of each film. Reliability of the scale was evaluated. Face validity of the scale was assessed and the construct validity of two hypotheses was tested. RESULTS: The final scale consisted of five questions in which a given X-ray could score from--10 (most difficult) to + 10 (easiest to interpret) by a single rater. Raters included 53 medical students, 10 paediatric residents and 10 emergency staff. The scale demonstrated excellent internal consistency (r = 0.94), inter-rater reliability (r = 0.95) and overall reliability (r = 0.90) in medical students. Construct validity testing demonstrated good correlation (r = 0.72) between diagnostic accuracy and mean XRDS score. Mean scores on the scale were significantly lower (indicating that CXRs were more difficult to interpret) for students than for resident and staff doctors (P < 0.0001). CONCLUSIONS: The scale developed in this study serves as a reliable and valid tool for categorising CXRs according to diagnostic difficulty, which reduces the bias inherent in the process of selecting radiographs by expert opinion alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".